bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
Title: Everyday taxi drivers: Do better navigators have larger hippocampi? RUNNING TITLE: EVERYDAY TAXI DRIVERS
Steven M. Weisberga, Nora S. Newcombeb, & Anjan Chatterjeec
aUniversity of Pennsylvania, bTemple University
Steven M. Weisberg ([email protected]), Anjan Chatterjee
([email protected]), Department of Neurology, University of Pennsylvania,
Philadelphia, PA 19104.
Nora S. Newcombe ([email protected]), Department of Psychology, Temple
University, Philadelphia, PA 19122.
Declarations of interest: none
Correspondence concerning this article should be addressed to Steven M. Weisberg, at
Center for Cognitive Neuroscience, University of Pennsylvania, Philadelphia, PA 19104 USA.
Email: [email protected]. Phone: 610-212-3113. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 1
Abstract
Work with non-human animals and human navigation experts (London taxi drivers)
suggests that the size of the hippocampus, particularly the right posterior hippocampus in
humans, relates to navigation expertise. Similar observations, sometimes implicating other
sections of the hippocampus, have been made for aging populations and for people with
neurodegenerative diseases that affect the hippocampus. These data support the hypothesis that
hippocampal volume relates to navigation ability. However, the support for this hypothesis is
mixed in healthy, young adults, who range widely in their navigation ability. Here, we
administered a naturalistic navigation task that measures cognitive map accuracy to a sample of
90 healthy, young adults who also had MRI scans. Using a sequential analysis design with a
registered analysis plan, we did not find that navigation ability related to hippocampal volume
(total, right only, right posterior only). We conclude that navigation ability in a typical
population does not correlate with variations in hippocampal size, and consider possible
explanations for this null result.
Keywords: hippocampus, human behavior, spatial cognition, spatial navigation, structural MRI
Abbreviations: ASHS – Automatic Segmentation of Hippocampal Subfields BF – Bayes Factor ERC – Entorhinal cortex MRT – Mental rotation test OSF – Open Science Framework PHC – Parahippocampal cortex SAQ – Spatial anxiety questionnaire SBSOD – Santa Barbara sense of direction scale WRAT-4 – Wide range achievement test 4 – verbal
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 2
1. Introduction
Spatial navigation is a fundamental problem faced by any mobile organism. This ability
is supported in part by the hippocampus, which is theorized to construct a cognitive map –
knowledge of the distances and directions between landmarks (O’Keefe & Nadel, 1978; Tolman,
1948). Evidence for the role of the hippocampus in navigation comes from functional neural data
across a wide range of levels of analysis. At the single-cell level, place cells in the hippocampus
fire when an animal is in a certain location (Ekstrom et al., 2003; O’Keefe & Nadel, 1978). At
the voxel level, fMRI reveals that the patterns of voxels in the hippocampus map to information
about spatial distance (Vass & Epstein, 2013). At the whole hippocampus anatomic level, fMRI
reveals that the hippocampus is more active during active navigation than passive travel or when
following a familiar route (e.g., Hartley et al. 2003). So, neuronal activity in the hippocampus is
consistent with the hypothesis that the hippocampus constructs a cognitive map.
Intriguingly, there is reason to suppose that variations in the structure and function of
hippocampus may relate to variations in navigation abilities. Maguire and colleagues (2000;
2006) showed that the right posterior hippocampus was enlarged in taxi drivers from London,
who memorize an enormous catalog of spatial information and navigate easily around the
complex layout of London. Further work showed that elderly taxi drivers who were still driving
taxis had enlarged right posterior hippocampi compared to elderly taxi drivers who had stopped
(Woollett, Spiers, & Maguire, 2009). Although this work on taxi drivers presented among the
first data to show a correlation between hippocampal volume and aspects of navigation behavior,
the sample sizes were small (less than 20 participants in most studies). And despite making
claims about increased right posterior hippocampal volume and decreased right anterior
hippocampal volume in taxi drivers, the researchers did not test the key interaction between bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 3
posterior and anterior hippocampal volume between taxi drivers and controls, rendering this
conclusion unsupported by the data. Nevertheless, complementary research in non-human
animals has supported the notion that larger hippocampi are associated with better navigation
(Sherry, Jacobs, & Gaulin, 1992). Importantly, changes in hippocampal size may occur within an
animal’s lifespan. Male meadow voles, for example, show cell proliferation in the dentate gyrus
concomitant with hippocampal volume increases in the breeding season (when males have
greater spatial navigation requirements) compared to the non-breeding season (Galea &
McEwen, 1999). Similarly, compromise of the hippocampus is associated with spatial navigation
deficits. Poor spatial navigation occurs in patients with Alzheimer’s disease (Deipolyi, Rankin,
Mucke, Miller, & Gorno-Tempini, 2007; Konishi et al., 2018; Moodley et al., 2015; Plancher,
Tirard, Gyselinck, Nicolas, & Piolino, 2012), patients with brain lesions (Kolarik, Baer,
Shahlaie, Yonelinas, & Ekstrom, 2018; Rosenbaum et al., 2000; Smith & Milner, 1981) and in
the elderly (Konishi, Mckenzie, Etcharnendy, Roy, & Bohbot, 2017; Moodley et al., 2015).
These groups show reduced hippocampal volume, suggesting that a healthy hippocampus is
critical for normal spatial navigation function.
Collectively, this body of literature seems to make a powerful case for an association
between hippocampal volume and navigation. However, evidence from healthy young adults is
more mixed. Some research studies are positive. First, there are findings that hippocampal
volume correlates with specific navigationally-relevant spatial tasks, notably perspective taking.
(in which an unseen view must be imagined). Perspective taking correlates with spatial memory
for large-scale environments (Weisberg & Newcombe, 2016) and elicits neural activation in the
hippocampus (Lambrey, Doeller, Berthoz, & Burgess, 2012). Hartley and Harlow (2012) created
a perspective-taking task in which participants match an image of three-dimensional mountains bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 4
to an image of the same mountains range viewed from a different perspective while ignoring
similar-looking foils. Accuracy on this task correlated with bilateral hippocampal volume.
Sherrill and colleagues (2018) measured participant’s ability to find a goal from first-person and
map perspectives after viewing a map with their position and the position of the goal. Accuracy
in the first-person condition correlated with bilateral posterior hippocampal volume. Second,
hippocampal volume correlates with specific strategies used by healthy young adults when
navigating. Bohbot and colleagues measured spatial navigation strategy using a task in which the
direction to goals could be based on which response should be made (e.g., to one’s right; thought
to rely on the caudate nucleus), or based on each goal’s position relative to external landmarks
(e.g., the church is to the right of the school; thought to rely on the hippocampus), independent of
success at finding a goal. They found large positive correlations with hippocampal volume and
the number of goals found relative to external landmarks (Bohbot, Lerch, Thorndycraft, Iaria, &
Zijdenbos, 2007). Third, using a self-report measure of navigation ability, two studies with large
samples reported correlations with hippocampal volume, although the effect sizes were modest
(Hao et al., 2016; Wegman et al., 2014).
None of these approaches is direct, however. Perspective taking is only one component of
successful navigation. Navigation strategy can be orthogonal to navigation accuracy (Marchette,
Bakker, & Shelton, 2011). Self-report is correlated with, but not the same as, navigation
accuracy. In a more direct look at the issue in typical adults using a real-world environment to
measure navigation ability found a large correlation with right posterior hippocampus. However,
that study suffers from the use of a small sample (Schinazi, Nardi, Newcombe, Shipley, &
Epstein, 2013), and we are unaware of other studies of this kind. Additionally, studies in this
literature vary in how they define the relevant hippocampal areas, analyzing right posterior bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 5
hippocampal volume (Maguire et al., 2000, 2006; Schinazi et al., 2013), total hippocampal
volume (Hao et al., 2016; Hartley & Harlow, 2012; Konishi et al., 2017; Wegman et al., 2014) or
both posterior hippocampi (Sherrill et al., 2018). Moreover, some studies correct for total brain
volume, gender, and age, whereas others do not. These different anatomic and analytic choices
undermine confidence in the premise that hippocampal structure correlate with navigation.
Here, we test the hypothesis that hippocampal volume is a biological marker for spatial
navigation ability in young, healthy human subjects. We test this hypothesis in a large sample
using a widely-used desktop virtual environment (Virtual Silcton; Weisberg et al. 2014;
Weisberg and Newcombe 2016). Virtual Silcton measures navigational accuracy while allowing
participants to vary in the strategy they use. As a primary behavioral measure of interest, we
chose total pointing performance – or how accurately participants could point to and from all
locations in Virtual Silcton. This measure captures the accuracy with which participants learned
the direction from each building to every other, and may substitute for the ability to take a novel
shortcut – a hallmark of the cognitive map. Unlike the task used by Bohbot and colleagues
(2007), the pointing task used in Virtual Silcton does not constrain participants to use one
navigation strategy or another.
We chose right total hippocampal volume as the primary target of analysis because right
hippocampal volume is more consistently reported to be related to navigation ability than left.
We thus chose right hippocampal volume as our primary confirmatory analysis, the simplest
measure of hippocampal volume, which would not introduce additional issues of reliability in
segmentation or choice of measurement technique. We registered one confirmatory analysis
using a sequential analysis design (Lakens, 2014) in which we planned to correlate right total
hippocampal volume with how well participants learned locations after navigating in Virtual bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 6
Silcton. However, whereas some data suggest that the posterior hippocampus on the right relates
most strongly to spatial navigation ability (Maguire et al., 2000; Schinazi et al., 2013), other
research shows this relationship with the right anterior hippocampus (Wegman et al., 2014) or
right total hippocampus (Hao et al., 2016; Hartley & Harlow, 2012; Konishi et al., 2017). Some
research has assessed as the primary measure of interest the ratio between anterior and posterior
hippocampus (Poppenk, Evensmoen, Moscovitch, & Nadel, 2013). For that reason, in
exploratory analyses, we also looked at anterior, posterior, and total hippocampal volume on the
right and left.
We also considered the possibility that non-hippocampal brain structures might relate to
navigation ability, or that alternative measures of navigation ability might better capture
hippocampal-based navigation. We thus conducted exploratory analyses relating accuracy on
several Virtual Silcton measures (subsets of the pointing task, a map constructed from memory,
and building naming) and non-Silcton measures (including mental rotation, verbal ability, self-
reported navigation ability, and self-reported spatial anxiety) to the volume of various brain
structures (including sub-divisions of left and right hippocampi, caudate nucleus, the amygdala,
and total cortical volume).
2. Materials and Methods
2.1. Participants
We recruited participants by advertising to and recruiting from participants who had
participated in fMRI experiments from the Center for Cognitive Neuroscience at the University
of Pennsylvania, asking them to participate in a one-hour study for which they would be paid
$10. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 7
We recruited 90 participants (54 women). Nineteen participants self-reported as Asian,
17 as African-American or Black, and 42 as Caucasian or White. Thirteen participants self-
reported as Hispanic, three reported multiple races, one reported other, and one participant did
not report ethnicity or race. Participants' average age was 23.1 years (SD = 3.94).
2.2.MRI Acquisition
Scanning was performed at the Hospital of the University of Pennsylvania using a 3T
Siemens Trio scanner equipped with a 64-channel head coil. High-resolution T1-weighted
images were acquired using a three-dimensional magnetization-prepared rapid acquisition
gradient echo pulse sequence. Because these data were collected for different research studies,
specific parameters varied by protocol (see Supplementary Table 1).
2.3. Volumetry Measures
We calculated neuroanatomical volume of cortical structures in two ways. For the main
analysis of the right hippocampus, we extracted hippocampal volume in two ways – Freesurfer
and Automatic Segmentation of Hippocampal Subfields (ASHS). For the exploratory analyses,
including sub-regions of the hippocampus and additional neuroanatomical structures, we focus
on the parcellation from ASHS in the main text, but include analyses from Freesurfer in the
supplementary results.
We used Freesurfer 6.0 (Iglesias et al., 2015) software to extract volume estimates of
cortical and subcortical regions as part of the standard recon-all pipeline. We segmented
posterior and anterior hippocampus manually using Freesurfer's hippocampal parcellation.
Anterior hippocampus was defined as all voxels in this parcellation that were in all slices anterior
to (and including) the last coronal slice with at least 3 pixels that could be identified as the uncus
(as defined in Morey et al. 2009). We then segmented posterior and anterior hippocampus bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 8
manually using Freesurfer's hippocampal parcellation. Anterior hippocampus was defined as all
voxels in this parcellation that were in all slices anterior to (and including) the last coronal slice
with at least 3 pixels that could be identified as the uncus (as defined in Morey et al. 2009). We
also used the ASHS pipeline, which performs automatic parcellation of the hippocampus and
other medial temporal lobe structures, including estimates of posterior and anterior hippocampus
(Yushkevich et al., 2015).
2.4. Behavioral and Self-report Measures
2.4.1. Demographics.
We asked participants to report their biological sex, gender, ethnicity, age, education
level, and handedness.
2.4.2. Wide range achievement test 4 – verbal (WRAT-4; Wilkinson and Robertson
2006).
The WRAT-4 Word Reading Subtest is a measure of verbal IQ that correlates highly with
the WAIS-III, and WISC-IV (Strauss, 2006). The WRAT-4 Word Reading Subtest requires
participants to pronounce fifty-five individual words. Each participant's score is the number
of words pronounced correctly out of 55. Any participants who reported speaking any
language besides English as their first language were excluded from these analyses (eight
participants were excluded based on this criterion).
2.4.3. Spatial anxiety questionnaire (SAQ; Lawton 1994).
This self-report measure of spatial anxiety consists of eight 7-point Likert-scale items that
ask participants to indicate their level of anxiety when confronting situations such as
"Locating your car in a very large parking garage or parking lot," and " Finding your way to
an appointment in an area of a city or town with which you are not familiar." bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 9
2.4.4. Santa Barbara sense of direction scale (SBSOD; Hegarty, Richardson, Montello,
Lovelace, & Subbiah, 2002).
This self-report measure of navigation ability consists of fifteen 7-point Likert-scale
items such as "I am very good at giving directions," and "I very easily get lost in a new city."
The average score for each participant has been shown to correlate highly with performance
on behavioral navigation tasks in real and virtual environments (Hegarty et al., 2002;
Weisberg et al., 2014).
2.4.5. Mental rotation test (MRT; Vandenberg and Kuse 1978; adapted by Peters et al.
1995).
This computerized version of the MRT consists of two 10-item sections of multiple-
choice questions. Participants have three minutes per section. Each item consists of one
target two-dimensional image of a three-dimensional shape made up of connected cubes, and
four answer choices, also made up of connected cubes. Two of the answer choices are the
same configuration of cubes, but rotated in 3D space. The other two answer choices are a
different configuration. Participants received two points per correct choice, and lost two
points per incorrect choice. Zero points were awarded for each omission.
2.4.6. Virtual Silcton (Schinazi et al., 2013; Weisberg & Newcombe, 2016; Weisberg et
al., 2014).
Virtual Silcton is a behavioral navigation paradigm administered via desktop computer,
mouse, and keyboard. Modeled after the route integration paradigm (e.g., Hanley and Levine
1983; Holding and Holding 1989; Ishikawa and Montello 2006; Schinazi et al. 2013),
participants learn two routes in separate areas of the same virtual environment by virtually
traveling along a road indicated by arrows (see Figure 1). They learn the names and locations bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 10
of four buildings along each of these routes. Then, they travel along two routes which
connect the two areas from the first two routes. Virtual travel consisted of pressing arrow
keys (or the W, A, S, and D keys) on a standard keyboard to move in the environment, and
moving the mouse to look around. Participants were constrained to travel only along routes
indicated with arrows. That is, we surrounded each route with invisible walls that restricted
movement off the routes, but could be seen through. Participants could move and look at
whatever pace they chose. Participants had the opportunity to learn each route once. At a
minimum, we required participants to travel from the beginning to the end and back to the
beginning of each route, but participants could spend as much time and do as much
backtracking as they liked. Buildings were indicated by blue gems, which hovered over the
path, and named with signs in front of the building.
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 11
Figure 1.
Screenshots and map of Virtual Silcton. Screenshots from Route A and B (A) and aerial map
of Virtual Silcton, never seen by participants (B). Buildings were indicated by blue gems, which
hovered along the path and named with yellow and red signs. Small white circles on the map
indicate the front door of each building, which was the exact spot participants were asked to
point to during the pointing task. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 12
Participants were tested on how well they learned directions among the buildings within
each of the main routes, and among buildings between the main routes. Testing involved two
tasks. For an onsite pointing task, participants pointed to all buildings from each building they
learned. The participant viewed the virtual environment along the route, next to one of the
buildings they learned, and moved the mouse to rotate the view and position a crosshair toward
one of the other buildings, then clicked to record the direction. The name of the building at the
top of the screen then changed, and the participant pointed to the next named building. The
dependent variable was calculated as the absolute error between the participant's pointing
judgment and the actual direction of the building (if this difference was greater than 180°, it was
corrected to measure the shorter of the two possible arcs). We calculated pointing error
separately for within-route trials and between-route trials separately. This resulted in 32
between-route trials and 24 within-route trials. Of the 24 within-route trials, 14 were mutually-
intervisible (i.e., if any part of the building being pointed at was visible from the building being
pointed from, it counted as a mutually-intervisible trial), while 10 were not.
Participants also completed a model-building task wherein they viewed a rectangular box
on a computer screen and birds-eye view images of the eight buildings. Scrolling over the
buildings with the mouse revealed a picture of the front view of the building and its name.
Participants were instructed to drag and drop buildings to the position in the box they believed
the building would be located (as if they were creating a map), without regard to the orientation
of the buildings or to the map. The model-building task was scored using bidimensional
regression analyses (Friedman and Kohler 2003).
Finally, in the building naming task, participants were shown pictures of each building
and asked to name the building to the best of their ability. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 13
2.4.7. Debriefing and strategy questionnaire.
We asked participants two debriefing questions: "What was the hardest part of the
navigation test?" and "Did you have trouble remembering the names of the buildings as well
as the positions? Describe strategies you used to try to remember the names and locations of
the buildings."
2.5. Experimental Procedure
Participants completed the MRI scan as part of a separate experiment either in our lab, or
in another lab at the University of Pennsylvania. Then, participants were recruited to participate
in the behavioral study in a separate session. In the behavioral session, participants first provided
and documented informed consent, then completed the demographics and WRAT-4 measures,
followed by the SAQ, SBSOD, and MRT. Then, participants completed Virtual Silcton and the
debriefing questionnaire.
2.6. Registration and Analysis Plan
This study was registered on the Open Science Framework (OSF; https://osf.io/ea99d/)
after data collection was completed for 50 participants and data analysis was completed for 33
participants. The analysis plan was created as described in the registration documents to formally
establish A) one of the multiple possible ways of analyzing the data to address our hypothesis,
and B) a sequential analysis data collection procedure.
We based our analysis plan on the simplest possible correlation between overall structural
volume of the right hippocampus (Fischl et al., 2002) with overall pointing performance on
Virtual Silcton. This analysis was chosen because it requires the least human subjectivity in data
coding, and, based on the empirical literature that the right hippocampus is most likely to relate
to navigation. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 14
We proposed a sequential analysis plan because the results from 33 participants were
ambiguous (a marginally significant correlation, but with a small sample size). Given the
difficulty of recruiting participants with MRI data, sequential analyses allow flexibility in
determining sample size. We used the extant literature to create large and small p-value
thresholds after which data collection would stop and the results would be reported. The large p-
value cutoff was based on the effect size between self-reported navigation ability and
hippocampal volume reported by Hao and colleagues (2016) as our smallest effect size of
interest. The small p-value cutoff was based on using q < .05 (p < .05 after applying the
sequential analysis correction for multiple comparisons; Lakens 2014). We used power = 80%
and would collect 20 participants batches until either we obtained a p-value that was smaller than
q < .05, or until we reached 90 participants total (at which point we would have an 80% chance
of detecting an effect significantly larger than our smallest effect size of interest).
Interim results that were reported on the registration on OSF used our analysis plan to
determine whether additional participants would need to be recruited. Since initial registration,
we learned of a pipeline that yields more accurate parcellations of hippocampal volume (which
we verified with visual exploration of the hippocampal segmentations), as well as providing
automated estimates of posterior and anterior volume (an exploratory question of interest).
Consequently, we used this new method of anatomic analysis which was not pre-registered.
2.7. Statistical Tools
All processed data and code are available on the Open Science Framework
(https://osf.io/ea99d/). All figures, analyses, and supplementary analyses are available in an
interactive Jupyter notebook (https://mybinder.org/v2/gh/smweis/Silcton_MRI/master). bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 15
Unless otherwise specified below, statistics were calculated using the scipy and numpy
packages in Python (McKinney, 2010; Oliphant, 2006). Data were manipulated with Pandas
(McKinney, 2010) and visualized using Matplotlib (Hunter, 2007). Repeated measures ANOVAs
were calculated using the ezANOVA package in R (version 4.4), using RStudio (RStudio Team
2016) . Effect sizes are, for t-tests, Cohen's d, corrected for correlations for within-sample tests,
2 and for ANOVAs, generalized eta squared (η g; Bakeman 2005).
3. Results
We first present results from the pre-registered analyses. Next, we describe several
multiple regression analyses we ran to match previous analyses (e.g., controlling for cortical
volume, age, and gender). We then present exploratory analyses using the following
progression. We focus on Virtual Silcton measures first, looking more broadly at subdivisions of
the hippocampus (right and left) before moving on to other subcortical regions and cortical
volume. Finally, we analyze non-Silcton measures, following the same progression from the
hippocampus to the rest of the brain.
3.1. Pre-registered Analyses
Our principal analysis was the correlation between right hippocampal volume and
overall pointing error on Virtual Silcton. We did not find a correlation between right
hippocampal volume and pointing error, r(90) = .02, p = .88. Converting this value to a t-statistic
yields a Bayes Factor (BF; calculated from http://pcl.missouri.edu/bf-one-sample; Rouder et al.
2009) in favor of the null hypothesis (BF01) of BF01 = 8.49. Using the original specified analysis
plan (automated Freesurfer hippocampal volume calculation) to extract hippocampal volume did
not change these results, r(90) = .07, p = .52, BF01 = 7.04. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 16
Although we did not specify whether outliers would be excluded in our pre-registration,
we re-ran this analysis excluding one outlier who had total right hippocampal volume that was
approximately 4 standard deviations below the mean. Omitting this individual resulted in a
slightly higher but still non-significant correlation, r(88) = .10, p = 0.38, and BF01 = 5.49 (see
Figure 2). Using the original specified analysis plan to extract hippocampal volume did not
change these results either, r(88) = .12, p = .28, BF01 = 4.80.
3.2. Multiple Regression Control Analyses
We wanted to determine whether there was a relation between right hippocampal volume
and the navigation measures after accounting for cognitive and demographic factors. These
control analyses are especially important because some (though not all) previous studies
controlled for age, gender, and cortical volume. To account for these additional sources of
nuisance variance, we ran several multiple regression analyses, controlling for gender, age,
verbal IQ, small-scale spatial ability, and cortical volume. Specifically, we modeled total
pointing error (and, in additional models, between-route and within-route pointing error) as a
linear combination of right hippocampal (or right posterior hippocampal) volume with MRT,
WRAT, gender, age, and cortical volume. No combination of regressors resulted in a significant
relation between hippocampal volume and pointing error. Results of the models are reported in
Table 1.
3.3. Exploratory Analyses
We conducted several exploratory analyses. We report uncorrected p-values, but interpret
findings from these analyses as exploratory results that invite replication in independent data. For
interpretability, with a sample size of n = 90, a Pearson's correlation of r = .21 would have a
probability of p < .05, uncorrected. Correcting for all possible pairwise correlations (between bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 17
major variables of interest) yielded a significance threshold of approximately r = .38. Because
these analyses were exploratory, we only describe correlations as significant if they passed the
Bonferroni-corrected threshold.
For hippocampal volume, we only used the results from the automated segmentation
pipeline (ASHS). The remainder of cortical volume calculations come from the Freesurfer
parcellation. See Supplemental Figure 1 for additional analyses using the Freesurfer and by-hand
segmentation.
Similar to previous research with Virtual Silcton, we observed differences in performance
on within-route pointing trials compared to between-route pointing trials (see Supplemental
Figure 2). On within-pointing trials, participants pointed to a building that was on the same main
route as the building they were standing near. On between-pointing trials, participants pointed to
a building that was on the other main route as the building they were standing near. We analyze
pointing data continuously, correlating pointing performance with brain and behavioral
measures. For the correlational analyses, we collapse across between-route and within-route
trials (total pointing error), but also analyzed them separately, as both show individual
differences. We also analyze pointing data categorically, splitting participants into three groups –
Integrators, who performed well on between-route and within-route pointing; Non-Integrators,
who performed well on within-route pointing but could not integrate the two routes, performing
poorly on between-route pointing; and Imprecise Navigators, who performed poorly on both
types of pointing trials. We created these three groups on the basis of a K-means cluster analysis
constrained to three groups on a large sample of approximately 300 participants from previous
Virtual Silcton studies (Weisberg & Newcombe, 2016). Using the cutoff values from these
groups yielded 34 Integrators, 42 Non-Integrators, and 14 Imprecise Navigators. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 18
Figure 2.
Relation between total pointing performance and right hippocampal volume. The overall
correlation (black line, black font) between total pointing performance (error in degrees,
reversed) and right hippocampal volume as measured by ASHS segmentation. One outlier is
excluded from this scatterplot, but results were not statistically different with the outlier
included. Despite numerical differences, the correlation coefficients obtained within each group
do not differ statistically from each other. Large circles indicate group means and dotted lines
indicate ± 1 standard error of the mean, calculated within group. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 19
3.3.1. Left and right, anterior and posterior hippocampus.
Correlations between left and right total, posterior, and anterior hippocampal volumes
were not related significantly with overall pointing, between-route pointing, or within-route
pointing (see Figure 3). The three pointing groups did not significantly differ in total left
2 hippocampal volume, F(2,87) = 0.42, p = .66, η g = .01, nor in total right hippocampal
2 volume, F(2,87) = 0.15, p = .86, η g = .003, nor in posterior left hippocampal volume,
2 F(2,87) = 2.53, p = .09, η g = .05, nor in posterior right hippocampal volume, F(2,87) = 0.68,
2 2 p = .51, η g = .02, nor in anterior left hippocampal volume, F(2,87) = 0.05, p = .95, η g =
2 .001, nor in anterior right hippocampal volume, F(2,87) = 0.06, p = .94, η g = .001. We also
assessed the correlation between the pointing measures and the ratio of posterior to anterior
hippocampal volume on the right and left. Some research has shown a link between a
relatively larger posterior hippocampus and performance on navigation ability tasks
(Poppenk et al., 2013). However, we did not observe such a relation (maximum r = -.13,
correlation between left posterior-anterior ratio with between-route pointing).
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 20
Figure 3.
Virtual Silcton pointing correlations with brain volume measures. Pearson's r
correlations between Virtual Silcton pointing measures and brain measures. Hippocampal
measures were calculated using ASHS, whereas additional brain area volume measures were
calculated using Freesurfer.
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 21
3.3.2. Cortical volume, brain volume, and other brain areas.
The only notable brain-behavior correlation we observed on the pointing task was a
positive relation with cortical volume, r(90) = .22, p = .037, though this did not exceed the
Bonferroni-corrected threshold of r = .38. The caudate, amygdala, and the other medial
temporal lobe structures (BA35, BA36, entorhinal cortex (ERC) or parahippocampal cortex
(PHC)) resulted in non-significant correlations.
3.3.3. Other Silcton measures.
The model building task (measuring overall configuration or measuring within-route
configuration separately) correlated negatively with hippocampal measures (-.20 < r < .00),
despite being positively correlated with pointing performance, r(90) = .58, p < .00001.
Overall, the correlation between pointing and hippocampal measures appeared stronger than
the correlation between model-building and the hippocampal measures. To test this
possibility statistically, we compared the correlation between pointing and each subdivision
of the hippocampus (left/right, anterior/posterior/both) with the correlation between model-
building and each subdivision of the hippocampus. Three subdivisions of the hippocampus
were significantly more correlated (p < .05, uncorrected) with pointing compared to model-
building: right anterior hippocampal volume, t(87) = 2.09, p = .04, left anterior hippocampus,
t(87) = 2.65, p = .01, and left total hippocampus, t(87) = 2.53, p = .01. Right total
hippocampus correlations between total pointing and model building were marginally
significantly different, t(87) = 1.87, p = .06. Due to the weak correlations overall, we
interpret this pattern conservatively as showing a possible differentiation of hippocampal
volume as it relates to distinct types of navigational representations.
3.3.4. Non-Silcton measures. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 22
We observed non-significant correlations (i.e., none surviving Bonferroni correction)
between the non-Silcton measures and the volume of various brain regions (see Figures 4 and
5). Self-report measures of navigation ability, the SBSOD and SAQ, were weakly correlated
with hippocampal volumes (Figure 5; -.06 < r < .13), whereas they were more strongly
correlated with caudate, amygdala, and total cortical and brain volume (.08 < r < 25; see
Figure 4). WRAT-4, the measure of verbal ability, was most strongly correlated with cortical
volume, r(90) = .16, p = .13, and brain volume, r(90), r = .15, p = .16, though neither of these
reached significance. Finally, the measure of small-scale spatial ability, MRT, was most
strongly correlated with cortical volume, r(90) = .30, p = .004.
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 23
Figure 4.
Virtual Silcton additional tasks correlations with brain volume measures. Pearson's r
correlations between Virtual Silcton measures and brain measures. Hippocampal measures were
calculated using ASHS, whereas additional brain area volume measures were calculated using
Freesurfer. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 24
Figure 5.
Non-Virtual Silcton tasks correlations with brain volume measures. Pearson's r correlations
between behavioral measures and brain measures. Hippocampal measures were calculated using
ASHS, whereas additional brain area volume measures were calculated using Freesurfer.
WRAT-4 = Wide ranging achievement test. SBSOD = Santa Barbara Sense of Direction scale.
SAQ = Spatial anxiety questionnaire. MRT = mental rotation test.
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 25
4. Discussion
The hippocampus plays a crucial role in spatial navigation in humans, but the volume of
the hippocampus may not be a biological marker for navigation ability among typical
populations. Using an established measure of individual differences in spatial navigation we did
not observe a correlation between gross anatomical properties of the hippocampus and pointing
and model-building measures – two major indicators of navigation accuracy. We note several
strengths of the current design. First, we used a navigational task that exhibits a wide-range of
individual differences in a relatively under-studied population (in this area) of young, healthy
adults. Second, we used a sample size large enough to detect small effect sizes and did so using a
pre-registered analytic plan.
While it is always difficult to determine the reason for a null result, we see three possible
interpretations for our results: 1) Hippocampal volume correlates with navigational ability in
extreme groups, but not in typical populations. 2) Structural properties of the hippocampus and
navigation behavior have a complex relationship. 3) Hippocampal volume correlates with
specific skills, not general navigation ability; successful navigation also requires cognitive
capabilities whose neuronal bases lie beyond the hippocampus.
4.1. Hippocampal volume correlates with navigational ability in extreme groups, but
not in typical populations
Data from multiple sources supports the idea that expert navigators have enlarged
hippocampi, whereas impaired navigators have smaller hippocampi. In humans, evidence for a
link between hippocampal volume and spatial navigation ability in experts first came from
studies of taxi drivers in London (e.g., Maguire et al. 2000) and in impaired navigators from
individuals with hippocampal lesions (Smith & Milner, 1981). Since then, additional research by bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 26
Maguire and colleagues has replicated and refined the evidence in taxi drivers, with several
studies showing enlarged right posterior hippocampi relative to different control groups
(Maguire et al., 2003; Woollett & Maguire, 2011), although this work suffers from small sample
sizes and a non-significant interaction between right posterior hippocampal volume and right
anterior hippocampal volume in taxi drivers and control groups. The association between
impaired navigators and smaller hippocampal volume has also been supported in studies on
pathology (Michel Habib & Sirigu, 1987; Mullally & Maguire, 2011; Packard & McGaugh,
1996), and in mild cognitive impairment and Alzheimer’s disease, which particularly affect the
hippocampus and its connections (Deipolyi et al., 2007; Nedelska et al., 2012; Parizkova et al.,
2018).
In the present study, we investigated navigation ability in a typical population of young,
healthy individuals. Our finding is consistent with more general assessments of navigation
ability, like those from self-report, that find a weak relation between hippocampal volume and
navigation ability in typical populations (Hao et al., 2016; Wegman et al., 2014). One way to
reconcile data from extreme groups with data from typical populations is to propose a nonlinear
relation between hippocampal volume and navigation ability (see Figure 6). At the extreme ends,
navigators who exclusively rely on hippocampal representations show growth in hippocampal
volume, whereas navigators who cannot rely on the hippocampus (because it has degenerated or
is gone) suffer the behavioral consequences. However, in the middle of the distribution, normal
variability in navigation (which is nevertheless wide) is not accounted for by hippocampal
volume. We consider other factors, which we discuss in the following two sections.
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 27
Figure 6.
Predicted model of navigation ability and hippocampal volume across impaired, typical,
and expert navigators. A visualization of the proposal that navigation ability relates to
hippocampal volume in a non-linear fashion such that impaired navigators (i.e., patients with
Alzheimer’s disease) and expert navigators (e.g., taxi drivers) show positive correlations with
hippocampal volume and navigation ability, whereas typical populations show no or weak linear
correlations. [N.B. The current study population and expert and impaired populations likely
overlap on navigation ability. If the groups completely overlap, this model is implausible. If
there is partial overlap, then we would expect higher correlations in expert and poor navigators,
which is very weakly the case, as in Figure 2. But this assumption should be tested empirically,
ideally through data collection on Virtual Silcton in patient groups and taxi drivers.] bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 28
4.2. Structural properties of the hippocampus and navigation behavior have a complex
relationship.
There are two distinct possibilities to consider here. First, the hippocampus may only be a
piece of the neural puzzle, providing specific computations and processes to a network of brain
regions to coordinate spatial navigation. Thus, rather than hippocampal structure relating to
variance in spatial navigation behavior, changes in the connections between, for example,
retrosplenial complex, the parietal lobe, and the hippocampus (Byrne, Becker, & Burgess, 2007;
Ekstrom, Huffman, & Starrett, 2017; Iaria et al., 2014) may characterize information flow around
the brain, which itself relates to spatial behavior. Indeed, medial temporal lobe lesions may yield
greater spatial deficits when those lesions include parahippocampal cortices (Aguirre &
D’Esposito, 1999; Habib & Sirigu, 1987). Similar to the difference between neural function and
neural structure within the hippocampus, however, it is unclear whether individual differences in
behavior would relate to connectivity structure (e.g., white matter tracts) or functional
connectivity activation patterns. Given the growing literature in this area, it would be a fruitful
and important avenue for future investigation.
The second possibility is that hippocampal volume may be too coarse a neuroanatomical
measure to show an association with navigation ability. Aspects of navigation behavior relate
only to the volume of specific subdivisions of the hippocampus, creating a complex picture of
structure-function relations. Indeed, across the literature, different hippocampal properties are
reported to correlate with navigation ability. Expert taxi drivers show increased right posterior
hippocampal volume but decreased anterior hippocampal volume (Maguire et al., 2000) although
these analyses exclude the body of the hippocampus (which we included as part of posterior
hippocampus). Yet, anterior hippocampal volume correlates with path integration (Brown, bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 29
Whiteman, Aselcioglu, & Stern, 2014; Chrastil, Sherrill, Aselcioglu, Hasselmo, & Stern, 2017)
and self-reported navigation ability (Wegman et al., 2014). And several studies show correlations
between navigation and total hippocampal volume (Hartley & Harlow, 2012; Head & Isom,
2010).
Additional data show correlations between navigation behavior and hippocampal
subfields which do not follow posterior/anterior divisions (Daugherty, Bender, Yuan, & Raz,
2016). In the present study, we did not assess hippocampal subfield correlations with navigation
ability because structural MRI data lacks the resolution to accurately parcellate the hippocampus
into subfields (Wisse, Biessels, & Geerlings, 2014). In light of these complexities, it is unclear
what if any aspects of hippocampal structure are important to navigation and what if any aspects
of navigation ability relate to hippocampal structure.
4.3. Hippocampal volume correlates with specific skills, not general navigation ability
Perspective-taking correlates with hippocampal volume (Hartley and Harlow 2012), as
does a particular navigation strategy (Konishi & Bohbot, 2013). In previous work with Virtual
Silcton we observed correlations with pointing task performance and a paper-and-pencil
perspective-taking test. Thus, perhaps hippocampal size is related to perspective taking, which is
one (but not the only) determinant of success in navigation on Silcton. Similarly, we have found
a complex relation between pointing accuracy on Silcton and navigation strategy, with
integrators performing well on shortcut tasks if they choose to take the short cuts—but not all do
(Weisberg & Newcombe, 2016). Again, partial overlap between hippocampal strategies and
navigation accuracy on Silcton would attenuate correlations of Silcton with hippocampal
volume. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 30
Navigation ability relies on a wide range of perceptual, cognitive, and meta-cognitive
processes, which likely do not all relate to the hippocampus (Ekstrom et al., 2017; Wolbers &
Hegarty, 2010). In the case of the taxi drivers, it is unclear whether their expertise encompasses
the creation of a cognitive map (or the capacity to do so) or maintaining an enormous catalog of
associational data (e.g., recalling the names of streets, landmarks, and regions). Either of these
may rely on the hippocampus, and cataloging associations correlates with the volume of
hippocampal subfields (dentate gyrus and CA2/3; Palombo et al. 2018). In the case of navigation
strategy, the ability to follow a familiar route through an environment, a viable alternate
navigational strategy, which does not depend on the creation of a cognitive map, relies on the
caudate (e.g., Marchette et al. 2011). The ability to recognize the same building from different
viewpoints, which correlates with self-reported navigation ability at least, relies on
representations in the parahippocampal place area (Epstein, Higgins, & Thompson-Schill, 2005),
rather than the hippocampus itself.
In navigation paradigms, like Virtual Silcton, where encoding and strategy choice are
unconstrained, navigation strategies that do not rely on the hippocampus could compensate for
impoverished cognitive maps. Variability in these non-hippocampally mediated cognitive
components of navigation could then underlie performance on Virtual Silcton in a typical
population. For example, we previously showed that variation in working memory relates to
performance on within-route pointing performance (Blacker, Weisberg, Newcombe, & Courtney,
2017; Weisberg & Newcombe, 2016), a process which likely does not rely on hippocampal
volume or even hippocampal function.
4.4. Cortical Volume and Navigation Ability bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 31
Although we did not predict a correlation between hippocampal volume and cortical
volume, cortical volume was the strongest correlate of the pointing task. To our knowledge, an
association between navigation ability and cortical volume overall has not been reported in the
hippocampal volume and navigation ability literature, although many studies correct for cortical
volume when analyzing hippocampal volume. Cortical volume is associated with measures of
general intelligence (Reardon et al., 2018), a finding consistent with the correlation we also
observed with cortical volume and mental rotation. We emphasize that this result was
exploratory, and the effect size small, but as the largest correlation we observed, we believe this
effect merits further study.
4.5. Limitations
Several aspects of the design of the current study limit the generalizability of our results.
First, although we observed a reasonable range of variability in both navigation ability and
hippocampal volume, they did not correlate with each other. Nevertheless, we might speculate
that the best navigators in the present sample (who arguably were at ceiling performance) would
have performed worse at a more difficult task than taxi drivers; and similarly, the worst
navigators in the present sample may have outperformed older adults or those with Alzheimer’s
disease. Second, navigation took place in a desktop virtual reality, rather than in the real world.
Although a large body of evidence supports the notion that hippocampal function can be elicited
from testing in virtual environments, it is reasonable to speculate that this setting may have
dampened the hippocampal contributions to navigation. Third, because we did not collect data on
strategy use, nor did we have functional imaging data during navigation, we are agnostic about
the strategies used by individual participants and whether their strategies engaged the
hippocampus. bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 32
Future studies can address these limitations by A) collecting a more varied sample,
including variations in age, general intelligence, and demographics; B) collecting data in both
real-world and virtual environments; and C) collecting functional neuroimaging during the
navigation and pointing phase to dissociate the role of hippocampal function from hippocampal
structure.
5. Conclusion
In sum, this study limits the generality of the link between hippocampal volume and
navigation accuracy in a typical population. These findings have implications for the role of the
hippocampus in general navigation, and for the extrapolation of findings in expert and impaired
groups to healthy, young adults.
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 33
Acknowledgements: The authors wish to acknowledge Russell Epstein and Geoff Aguirre for
assistance with data collection of the original MRI data.
Funding: This work was supported by the National Institutes of Health [F32DC015203 to
S.M.W., and R01DC012511 to A.C.] the Spatial Intelligence and Learning Center [SBE-
1041707 to N.S.N. and A.C]. The authors also wish to acknowledge funders who supported data
collection of the original MRI data: NIH grants [U01EY025864 – Low Vision Connectome, and
R01EY022350].
bioRxiv preprint doi: https://doi.org/10.1101/431155; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. EVERYDAY TAXI DRIVERS 34
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Table 1. Multiple regressions control analyses assessing total pointing performance with
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Dependent variable Predictor variable b SE t p R2 Adj. R2 Total pointing (with total right 0.21 0.15 hippocampal volume) (constant) -0.09 0.14 -0.64 0.52 Gender (male = 1) 0.28 0.26 1.08 0.29 Right Total Hippocampal -0.05 0.12 -0.40 0.69 Volume MRT 0.30 0.11 2.83 < .01 WRAT 0.22 0.11 2.09 0.04 Age 0.15 0.10 1.44 0.15 Brain Volume 0.01 0.10 0.07 0.95 Total pointing (with total right posterior 0.21 0.15 hippocampal volume) (constant) -0.09 0.14 -0.64 0.52 Gender (male = 1) 0.27 0.25 1.07 0.29 Right Posterior Hippocampal -0.05 0.11 -0.46 0.65 Volume MRT 0.30 0.11 2.90 0.006 WRAT 0.22 0.11 2.08 0.04 Age 0.14 0.10 1.43 0.16 Brain Volume 0.01 0.13 0.04 0.97
Note. Results of two separate multiple regression analyses revealing no effect of right hippocampal volume, controlling for various other measures. SE = Standard error. MRT = Mental Rotation Test. WRAT = Wide-ranging achievement test.